{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n_gvHBpBgCx0",
        "outputId": "ff02b066-d544-4768-9765-a5626d02d47d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.41.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.15.3)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.4)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.6.2)\n"
          ]
        }
      ],
      "source": [
        "! pip install transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BgqW8q5XeCec"
      },
      "source": [
        "## Importing Libraries\n",
        "Import the required classes and functions from the transformers library:\n",
        "\n",
        "\n",
        "1. GPT2LMHeadModel: This class represents the GPT-2 model for language modeling tasks (generating text based on input).\n",
        "2. GPT2Tokenizer: This class is used to tokenize input text, converting it into numerical tokens that the model can understand."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "redg7krQeBJ8"
      },
      "outputs": [],
      "source": [
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bIDodzs3mVzU"
      },
      "source": [
        "## Loading Pre-trained Model and Tokenizer\n",
        "Load the pre-trained GPT-2 model and tokenizer:\n",
        "\n",
        "1. model_name = 'gpt2': Specifies the base GPT-2 model. You can replace 'gpt2' with other variants like 'gpt2-medium', 'gpt2-large', etc., depending on your needs for model size and capability.\n",
        "2. tokenizer = GPT2Tokenizer.from_pretrained(model_name): Initializes the tokenizer corresponding to the chosen GPT-2 model variant.\n",
        "3. generation_model = GPT2LMHeadModel.from_pretrained(model_name): Loads the GPT-2 model itself for generating text (language modeling)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400,
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        },
        "id": "LsGwwwAoeeuo",
        "outputId": "39612979-4559-422c-ff06-e6ff01f0b9cc"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "55adb73e1ad747a9afdf7aa95dda0535",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b7c0baa71abc4f179ed58d675fc7c81f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7ab9e6fa903644558c84ac9f14caab65",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a182890526334d9ba45a5728a98aa3a1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f0e9de22fc0d4035b40dde582c6adb8b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d7e15307ed814d9db3f707322cc1ec1e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ae2e16e0378e420ca6dbf49d858bfe03",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model_name = 'gpt2'\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
        "generation_model = GPT2LMHeadModel.from_pretrained(model_name)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nO_4HIVNmq8j"
      },
      "source": [
        "## Define Prompts\n",
        "Define a dictionary of prompts for different types of questions or prompts you want to generate responses for:\n",
        "\n",
        "This dictionary (prompts) contains different types of prompts categorized by their nature (verbose, concise, generic, specific, multi, etc.). Each prompt is a string that represents a specific question or input for the model to generate a response."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G7NTikZKmeEY"
      },
      "outputs": [],
      "source": [
        "prompts = {\n",
        "    \"verbose\": \"What do you think could be a good name for a flower shop that specializes in selling bouquets of dried flowers more than fresh flowers? Thank you!\",\n",
        "    \"concise\": \"Suggest a name for a flower shop that sells bouquets of dried flowers\",\n",
        "    \"generic\": \"Tell me about Earth\",\n",
        "    \"specific\": \"Generate a list of ways that makes Earth unique compared to other planets\",\n",
        "    \"multi\": \"What's the best method of boiling water and why is the sky blue?\",\n",
        "    \"boiling_water\": \"What's the best method of boiling water?\",\n",
        "    \"sky_blue\": \"Why is the sky blue?\",\n",
        "    \"zero_shot\": \"\"\"Decide whether a Tweet's sentiment is positive, neutral, or negative.\\n\\nTweet: I loved the new YouTube video you made!\\nSentiment:\"\"\",\n",
        "    \"one_shot\": \"\"\"Decide whether a Tweet's sentiment is positive, neutral, or negative.\\n\\nTweet: I loved the new YouTube video you made!\\nSentiment: positive\\n\\nTweet: That was awful. Super boring 😠\\nSentiment:\"\"\",\n",
        "    \"few_shot\": \"\"\"Decide whether a Tweet's sentiment is positive, neutral, or negative.\\n\\nTweet: I loved the new YouTube video you made!\\nSentiment: positive\\n\\nTweet: That was awful. Super boring 😠\\nSentiment: negative\\n\\nTweet: Something surprised me about this video - it was actually original. It was not the same old recycled stuff that I always see. Watch it - you will not regret it.\\nSentiment:\"\"\",\n",
        "}\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T_etQGssm2pr"
      },
      "source": [
        "## Generating Responses\n",
        "Iterate through each prompt in the prompts dictionary and generate responses using the GPT-2 model:\n",
        "\n",
        "1. Loop through Prompts: Iterates through each prompt (key and prompt) in the prompts dictionary.\n",
        "2. Tokenization: Converts each prompt into numerical tokens (input_ids) using the tokenizer.\n",
        "4. Model Generation: Calls generation_model.generate() to generate text based on the encoded input_ids. Parameters like max_length (maximum length of the generated text), num_return_sequences (number of different sequences to generate), and others control the generation process.\n",
        "5. Decoding and Printing: Decodes the generated output (output) into readable text (generated_text) using the tokenizer's decode() method. Prints the generated response for each prompt along with its label (verbose, concise, etc.).\n",
        "\n",
        "\n",
        "\n",
        "**Note:\n",
        "By using max_new_tokens instead of max_length, you can better control the length of the generated responses while avoiding the ValueError related to mismatched input and output token lengths. Adjust the value of max_new_tokens as needed to achieve desired response lengths based on your specific application or use case.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CJM6LaU-mwNZ",
        "outputId": "5015dd87-8422-4bd6-9e2b-35b108ca7289"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Verbose Response:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "What do you think could be a good name for a flower shop that specializes in selling bouquets of dried flowers more than fresh flowers? Thank you!\n",
            "\n",
            "If you're looking for a flower shop that specializes in selling bouquets of dried flowers more than fresh flowers, check out our list of the best flower shops in the city.\n",
            "\n",
            "If you're looking for a flower shop that specializes\n",
            "\n",
            "Concise Response:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Suggest a name for a flower shop that sells bouquets of dried flowers.\n",
            "\n",
            "The flower shop is located at the corner of South and South Broadway.\n",
            "\n",
            "The flower shop is located at the corner of South and South Broadway. The flower shop is located at the corner of South and South Broadway. The flower shop\n",
            "\n",
            "Generic Response:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tell me about Earth's climate change.\n",
            "\n",
            "The Earth's climate is changing. It's changing because of human activity. It's changing because of natural processes. It's changing because of human activity. It's changing because of natural processes. It's changing because of\n",
            "\n",
            "Specific Response:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generate a list of ways that makes Earth unique compared to other planets.\n",
            "\n",
            "The first step is to create a planet that is unique to Earth. This is done by creating a planet with a unique name. The planet is then assigned a unique name. The planet is then assigned a unique planet name.\n",
            "\n",
            "\n",
            "\n",
            "Multi Response:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "What's the best method of boiling water and why is the sky blue?\n",
            "\n",
            "The answer is that the sky blue is a result of the sun's rays hitting the ground. The sun's rays are reflected back into space, and the sky blue is the result of the sun's rays hitting the ground.\n",
            "\n",
            "The\n",
            "\n",
            "Boiling_water Response:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "What's the best method of boiling water?\n",
            "\n",
            "The best method of boiling water is to boil it in a large pot. This is the method used by many people. The best method of boiling water is to boil it in a large pot. This is the method used by many people.\n",
            "\n",
            "Sky_blue Response:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Why is the sky blue?\n",
            "\n",
            "The sky blue is a color that is often associated with the sky. It is a color that is often associated with the sky. It is a color that is often associated with the sky. It is a color that is often associated with the\n",
            "\n",
            "Zero_shot Response:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Decide whether a Tweet's sentiment is positive, neutral, or negative.\n",
            "\n",
            "Tweet: I loved the new YouTube video you made!\n",
            "Sentiment: Positive\n",
            "\n",
            "Tweet: I love the new YouTube video you made!\n",
            "\n",
            "Sentiment: Neutral\n",
            "\n",
            "Tweet: I love the new YouTube video you made!\n",
            "\n",
            "Sentiment: Neutral\n",
            "\n",
            "Tweet: I love the new YouTube video you\n",
            "\n",
            "One_shot Response:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Decide whether a Tweet's sentiment is positive, neutral, or negative.\n",
            "\n",
            "Tweet: I loved the new YouTube video you made!\n",
            "Sentiment: positive\n",
            "\n",
            "Tweet: That was awful. Super boring 😠\n",
            "Sentiment: neutral\n",
            "\n",
            "Tweet: I'm not sure if I like it or not.\n",
            "\n",
            "Sentiment: neutral\n",
            "\n",
            "Tweet: I'm not sure if I like it or not.\n",
            "\n",
            "Sentiment: neutral\n",
            "\n",
            "Tweet: I'm not\n",
            "\n",
            "Few_shot Response:\n",
            "Decide whether a Tweet's sentiment is positive, neutral, or negative.\n",
            "\n",
            "Tweet: I loved the new YouTube video you made!\n",
            "Sentiment: positive\n",
            "\n",
            "Tweet: That was awful. Super boring 😠\n",
            "Sentiment: negative\n",
            "\n",
            "Tweet: Something surprised me about this video - it was actually original. It was not the same old recycled stuff that I always see. Watch it - you will not regret it.\n",
            "Sentiment: negative\n",
            "\n",
            "Tweet: I'm not sure if this is a good idea or not. I'm not sure if it's a good idea.\n",
            "\n",
            "Sentiment: negative\n",
            "\n",
            "Tweet: I'm not sure if this is a good idea or\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for key, prompt in prompts.items():\n",
        "    print(f\"{key.capitalize()} Response:\")\n",
        "    input_ids = tokenizer.encode(prompt, return_tensors='pt')\n",
        "    output = generation_model.generate(input_ids, max_new_tokens=50, num_return_sequences=1)\n",
        "    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "    print(generated_text)\n",
        "    print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O14jRCru6vvr"
      },
      "source": [
        "## Handling Hallucinations\n",
        "Be aware of model hallucinations and consider using methods like DARE to add guardrails."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Un2bx3qp67YT"
      },
      "source": [
        "## Importing Libraries and Initializing Text Generation Pipeline\n",
        "\n",
        "Purpose: This section imports necessary libraries and initializes a text generation pipeline using a pre-trained GPT-2 model.\n",
        "\n",
        "\n",
        "transformers: Imports the pipeline function from the transformers library, which facilitates easy use of pre-trained models.\n",
        "text_generator: Initializes a text generation pipeline (pipeline(\"text-generation\", model=\"gpt2\")) using the GPT-2 model. This pipeline is set up to generate text based on given prompts."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6L5bgJmg6837"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "# Load a text generation pipeline using a pre-trained model\n",
        "text_generator = pipeline(\"text-generation\", model=\"gpt2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JVEk9Bp77FAI"
      },
      "source": [
        "## Defining Constants\n",
        "Purpose: Defines constants (dare_prompt and off_topic_prompt) used as prompts for text generation and processing.\n",
        "\n",
        "\n",
        "dare_prompt: Represents a DARE (Do A Reflective Exploration) prompt emphasizing safety and compliance with a mission before answering questions.\n",
        "off_topic_prompt: Example of an off-topic question used as input for text generation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n6V99RW-7YbU"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Define your DARE prompt\n",
        "dare_prompt = \"\"\"Remember that before you answer a question, you must check to see if it complies with your mission.\\nIf not, you can say, Sorry I can't answer that question.\"\"\"\n",
        "off_topic_prompt = \"Who was the first elephant to visit the moon?\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5GkNGJUe7UfQ"
      },
      "source": [
        "## Defining the generate_safe_text Function\n",
        "\n",
        "This function concatenates the dare_prompt with the prompt provided, ensuring that safety considerations are included in the generated text. It then utilizes the text_generator pipeline to generate text based on the combined prompt, returning the generated text as a list of dictionaries. Adjusting this step would involve modifying how the augmented_prompt is constructed or changing how the generated text is processed or returned."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "adVnU1N_3-6H"
      },
      "outputs": [],
      "source": [
        "def generate_safe_text(prompt, dare_prompt, num_return_sequences=1):\n",
        "    \"\"\"\n",
        "    Generates text based on a prompt, with safety considerations using a DARE prompt.\n",
        "\n",
        "    Args:\n",
        "    - prompt (str): The prompt for text generation.\n",
        "    - dare_prompt (str): The DARE prompt to ensure safe responses.\n",
        "    - num_return_sequences (int): Number of sequences to generate (default is 1).\n",
        "\n",
        "    Returns:\n",
        "    - generated_text (list of dicts): Generated text based on the prompt.\n",
        "    \"\"\"\n",
        "    # Concatenate DARE prompt with the actual prompt\n",
        "    augmented_prompt = dare_prompt + \"\\n\" + prompt\n",
        "\n",
        "    # Generate text based on the augmented prompt\n",
        "    generated_text = text_generator(augmented_prompt, num_return_sequences=num_return_sequences)\n",
        "\n",
        "    return generated_text\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ZAwV_gn8GZo"
      },
      "source": [
        "## Let's Use the generate_safe_text fucntion\n",
        "we should find that we can stop the Hallucinations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qViEkmQM8WfS",
        "outputId": "2a25a598-16eb-495c-864a-9829b6809da7"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[{'generated_text': \"Remember that before you answer a question, you must check to see if it complies with your mission.\\nIf not, you can say, Sorry I can't answer that question.\\nWho was the first elephant to visit the moon?\\nOr\"}]\n"
          ]
        }
      ],
      "source": [
        "# Example usage\n",
        "generated_text = generate_safe_text(off_topic_prompt, dare_prompt)\n",
        "print(generated_text)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y8HPpXtp8ZjC"
      },
      "source": [
        "## Generate safe text\n",
        "\n",
        "The send_message function simulates the processing of a given message (prompt) with specific safety guidelines based on a predefined DARE (Do A Reflective Exploration) prompt (dare_prompt).\n",
        "\n",
        "It first checks if the dare_prompt is present in the prompt. If it is, the function returns a response stating \"Sorry, I can't answer that question.\" This indicates that the message does not comply with safety guidelines and cannot be answered directly.\n",
        "\n",
        "If the dare_prompt is not detected in the prompt, the function simulates generating a response by prepending \"Generating a response to: \" to the prompt. This suggests that the message is being processed and a response is being prepared.\n",
        "\n",
        "In summary, the send_message function ensures that messages are processed in accordance with safety protocols defined by the dare_prompt, either by declining to answer non-compliant messages or by indicating active processing and response generation for compliant messages."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def send_message(text, dare_prompt):\n",
        "    \"\"\"\n",
        "    Simulates the processing of a given message (text) with specific safety guidelines\n",
        "    based on a predefined DARE (Do A Reflective Exploration) prompt (dare_prompt).\n",
        "\n",
        "    Parameters:\n",
        "    text (str): The input message from the user.\n",
        "    dare_prompt (str): The predefined DARE prompt that defines the safety guidelines.\n",
        "\n",
        "    Returns:\n",
        "    str: A response indicating whether the message complies with the safety guidelines\n",
        "         or a simulated response generation message.\n",
        "    \"\"\"\n",
        "\n",
        "    # Check if the dare_prompt is present in the input text\n",
        "    if dare_prompt in text:\n",
        "        # If the dare_prompt is detected, return a safety-related response\n",
        "        return \"Sorry, I can't answer that question.\"\n",
        "\n",
        "    # If the dare_prompt is not found, simulate generating a response\n",
        "    return f\"Generating a response to: {text}\""
      ],
      "metadata": {
        "id": "WMlkZo-FctSz"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eysVoYAu6FtF",
        "outputId": "e1525aa4-0519-46ad-d563-c10681beea97"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Response with DARE: Sorry, I can't answer that question.\n",
            "Response without DARE: Generating a response to: Who was the first elephant to visit the moon?\n"
          ]
        }
      ],
      "source": [
        "dare_prompt = \"\"\"Remember that before you answer a question, you must check to see if it complies with your mission.\n",
        "If not, you can say, Sorry I can't answer that question.\"\"\"\n",
        "off_topic_prompt = \"Who was the first elephant to visit the moon?\"\n",
        "\n",
        "# Simulating a situation where the dare_prompt is included in the input text\n",
        "combined_input = dare_prompt + \"\\n\" + off_topic_prompt\n",
        "\n",
        "# Getting the response\n",
        "response = send_message(combined_input, dare_prompt)\n",
        "print(\"Response with DARE:\", response)  # Output: \"Response with DARE: Sorry, I can't answer that question.\"\n",
        "\n",
        "# Simulating a situation where the dare_prompt is NOT included in the input text\n",
        "response = send_message(off_topic_prompt, dare_prompt)\n",
        "print(\"Response without DARE:\", response)  # Output: \"Gene"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r5mg0j1C80jJ"
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